There is uncertainty about risk heterogeneity for venous thromboembolism (VTE) in older patients with advanced cancer and whether patients can be stratified according to VTE risk. We performed a retrospective cohort study of the linked Medicare-Surveillance, Epidemiology, and End Results cancer registry in older patients with advanced cancer of lung, breast, colon, prostate, or pancreas diagnosed between 1995-1999. We used survival analysis with demographics, comorbidities, and tumor characteristics/treatment as independent variables. Outcome was VTE diagnosed at least one month after cancer diagnosis. VTE rate was highest in the first year (3.4%). Compared to prostate cancer (1.4 VTEs/100 person-years), there was marked variability in VTE risk (hazard ratio (HR) for male-colon cancer 3.73 (95% CI 2.1-6.62), female-colon cancer HR 6.6 (3.83-11.38), up to female-pancreas cancer HR 21.57 (12.21-38.09). Stage IV cancer and chemotherapy resulted in higher risk (HRs 1.75 (1.44-2.12) and 1.31 (1.0-1.57), resp.). Stratifying the cohort by cancer type and stage using recursive partitioning analysis yielded five groups of VTE rates (nonlocalized prostate cancer 1.4 VTEs/100 person-years, to nonlocalized pancreatic cancer 17.4 VTEs/100 patient-years). In a high-risk population with advanced cancer, substantial variability in VTE risk exists, with notable differences according to cancer type and stage.
Hepatitis C virus (HCV) infection is a common problem in patients treated with maintenance hemodialysis (HD) and is associated with an increased morbidity and mortality and lower quality of life. The major causes of HCV-associated mortality are liver and cardiovascular-related death. HCV-infected HD patients have a higher prevalence of inflammation-related metabolic and vascular diseases, leading to high rates of cardiovascular mortality in patients with end-stage renal disease. In the current era of highly effective direct-acting antiviral regimens, HCV treatment may also confer hepatic, cardiovascular and other morbidity and mortality benefits even to dialysis-dependent patients who do not qualify for kidney transplantation. Currently, the most accepted regimens in this patient population include elbasvir/grazoprevir and glecaprevir/pibrentasvir.
The use of procurement biopsies for assessing kidney quality has been implicated as a driver of the nearly 20% kidney discard rate in the United States. Yet in some contexts, biopsies may boost clinical confidence, enabling acceptance of kidneys that would otherwise be discarded. We leveraged a novel organ offer simulation platform to conduct a controlled experiment isolating biopsy effects on offer acceptance decisions.Between November 26 and December 14, 2018, 41 kidney transplant surgeons and 27 transplant nephrologists each received the same 20 hypothetical kidney offers using a crossover design with weekend "washout" periods. Mini-study 1 included four, low serum creatinine (<1.5 mg/dl) donor offers with arguably "poor" biopsy findings that were based on real offers that were accepted with successful 3-year recipient outcome. For each of the four offers, two experimental variants-no biopsy and "good" biopsy-were also sent. Mini-study 2 included four AKI offers with no biopsy, each having an offer variant with "good" biopsy findings.Among low serum creatinine donor offers, we found approximately threefold higher odds of acceptance when arguably poor biopsy findings were hidden or replaced with good biopsy findings. Among AKI donor offers, we found nearly fourfold higher odds of acceptance with good biopsy findings compared with no biopsy. Biopsy information had profound but variable effects on decision making: more participants appeared to have been influenced by biopsies to rule out, versus rule in, transplantable kidneys.The current use of biopsies in the United States appears skewed toward inducing kidney discard. Several areas for improvement, including reducing variation in offer acceptance decisions and more accurate interpretation of findings, have the potential to make better use of scarce, donated organs. Offer simulation studies are a viable research tool for understanding decision making and identifying ways to improve the transplant system.
Deceased-donor kidneys experience extensive injury, activating adaptive and maladaptive pathways therefore impacting graft function. We evaluated urinary donor uromodulin (UMOD) and osteopontin (OPN) in recipient graft outcomes.Primary outcomes: all-cause graft failure (GF) and death-censored GF (dcGF). Secondary outcomes: delayed graft function (DGF) and 6-month estimated glomerular filtration rate (eGFR). We randomly divided our cohort of deceased donors and recipients into training and test datasets. We internally validated associations between donor urine UMOD and OPN at time of procurement, with our primary outcomes. The direction of association between biomarkers and GF contrasted. Subsequently, we evaluated UMOD:OPN ratio with all outcomes. To understand these mechanisms, we examined the effect of UMOD on expression of major histocompatibility complex II in mouse macrophages.Doubling of UMOD increased dcGF risk (adjusted hazard ratio [aHR], 1.1; 95% confidence interval [CI], 1.02-1.2), whereas OPN decreased dcGF risk (aHR, 0.94; 95% CI, 0.88-1). UMOD:OPN ratio ≤3 strengthened the association, with reduced dcGF risk (aHR, 0.57; 0.41-0.80) with similar associations for GF, and in the test dataset. A ratio ≤3 was also associated with lower DGF (aOR, 0.73; 95% CI, 0.60-0.89) and higher 6-month eGFR (adjusted β coefficient, 3.19; 95% CI, 1.28-5.11). UMOD increased major histocompatibility complex II expression elucidating a possible mechanism behind UMOD's association with GF.UMOD:OPN ratio ≤3 was protective, with lower risk of DGF, higher 6-month eGFR, and improved graft survival. This ratio may supplement existing strategies for evaluating kidney quality and allocation decisions regarding deceased-donor kidney transplantation.
Purpose: Accurate, reliable kidney transplant assessment tools are needed. Methods: This was a multicenter study to determine associations for α and π glutathione S-transferase (GST), measured from perfusate at beginning and end (base, post) of pump perfusion, with delayed graft function (DGF). We also compared GST levels from discarded vs. transplanted kidneys. Results: 428 kidneys were linked to outcomes in the national database. DGF occurred in 141 (32%). GST levels increased during perfusion. The adjusted relative risk (95% CI) of DGF with each log-unit increase in post π -GST was 1.36 (1.14-1.63). α-GST was not independently associated with DGF. GST levels were not significantly different (or were actually lower) in discarded compared with transplanted kidneys, whereas resistance was significantly higher in discards. Conclusions: π-GST at the end of perfusion was independently associated with DGF. Further studies should elucidate the utility of GST for identifying injured kidneys with regard to organ allocation, discard and recipient management decisions.Figure: No Caption available.Table: No Caption available.
Although hypothermic machine perfusion (HMP) is associated with improved kidney graft viability and function, the underlying biological mechanisms are unknown. Untargeted metabolomic profiling may identify potential metabolites and pathways that can help assess allograft viability and contribute to organ preservation. Therefore, in this multicenter study, we measured all detectable metabolites in perfusate collected at the beginning and end of deceased-donor kidney perfusion and evaluated their associations with graft failure. In our cohort of 190 kidney transplants, 33 (17%) had death-censored graft failure over a median follow-up of 5.0 years (IQR 3.0-6.1 years). We identified 553 known metabolites in perfusate and characterized their experimental and biological consistency through duplicate samples and unsupervised clustering. After perfusion-time adjustment and false discovery correction, six metabolites in post-HMP perfusate were significantly associated with death-censored graft failure, including alpha-ketoglutarate, 3-carboxy-4-methyl-5-propyl-2-furanpropanoate, 1-carboxyethylphenylalanine, and three glycerol-phosphatidylcholines. All six metabolites were associated with an increased risk of graft failure (Hazard Ratio per median absolute deviation range 1.04-1.45). Four of six metabolites also demonstrated significant interaction with donation after cardiac death with notably greater risk in the donation after cardiac death group (Hazard Ratios up to 1.69). Discarded kidneys did not have significantly different levels of any death-censored graft failure-associated metabolites. On interrogation of pathway analysis, production of reactive oxygen species and increased metabolism of fatty acids were upregulated in kidneys that subsequently developed death-censored graft failure. Thus, further understanding the role of these metabolites may inform the HMP process and help improve the objective evaluation of allograft offers, thereby reducing the discard of potentially viable organs. Although hypothermic machine perfusion (HMP) is associated with improved kidney graft viability and function, the underlying biological mechanisms are unknown. Untargeted metabolomic profiling may identify potential metabolites and pathways that can help assess allograft viability and contribute to organ preservation. Therefore, in this multicenter study, we measured all detectable metabolites in perfusate collected at the beginning and end of deceased-donor kidney perfusion and evaluated their associations with graft failure. In our cohort of 190 kidney transplants, 33 (17%) had death-censored graft failure over a median follow-up of 5.0 years (IQR 3.0-6.1 years). We identified 553 known metabolites in perfusate and characterized their experimental and biological consistency through duplicate samples and unsupervised clustering. After perfusion-time adjustment and false discovery correction, six metabolites in post-HMP perfusate were significantly associated with death-censored graft failure, including alpha-ketoglutarate, 3-carboxy-4-methyl-5-propyl-2-furanpropanoate, 1-carboxyethylphenylalanine, and three glycerol-phosphatidylcholines. All six metabolites were associated with an increased risk of graft failure (Hazard Ratio per median absolute deviation range 1.04-1.45). Four of six metabolites also demonstrated significant interaction with donation after cardiac death with notably greater risk in the donation after cardiac death group (Hazard Ratios up to 1.69). Discarded kidneys did not have significantly different levels of any death-censored graft failure-associated metabolites. On interrogation of pathway analysis, production of reactive oxygen species and increased metabolism of fatty acids were upregulated in kidneys that subsequently developed death-censored graft failure. Thus, further understanding the role of these metabolites may inform the HMP process and help improve the objective evaluation of allograft offers, thereby reducing the discard of potentially viable organs. Lay SummaryAlthough hypothermic machine perfusion (HMP) is associated with improved kidney transplant outcomes, the biological mechanisms are unknown. Metabolomic profiling of donor's kidneys may illuminate metabolites that can aid in assessing organ viability and preservation. In this study, all detectable metabolites were measured from perfusate collected at the beginning and end of deceased-donor kidney HMP. Six metabolites were significantly associated with the risk of death-censored graft failure, including alpha-ketoglutarate, 3-carboxy-4-methyl-5-propyl-2-furanpropanoate, 1-carboxyethylphenylalanine, and 3 glycerol-phosphatidylcholines. On interrogation of pathway analysis, the production of reactive oxygen species and increased metabolism of fatty acids were upregulated in kidneys that subsequently developed death-censored graft failure. Thus, further understanding the role of these metabolites may inform the HMP process and help improve the objective evaluation of allograft offers, thereby reducing the discard of potentially viable organs. Although hypothermic machine perfusion (HMP) is associated with improved kidney transplant outcomes, the biological mechanisms are unknown. Metabolomic profiling of donor's kidneys may illuminate metabolites that can aid in assessing organ viability and preservation. In this study, all detectable metabolites were measured from perfusate collected at the beginning and end of deceased-donor kidney HMP. Six metabolites were significantly associated with the risk of death-censored graft failure, including alpha-ketoglutarate, 3-carboxy-4-methyl-5-propyl-2-furanpropanoate, 1-carboxyethylphenylalanine, and 3 glycerol-phosphatidylcholines. On interrogation of pathway analysis, the production of reactive oxygen species and increased metabolism of fatty acids were upregulated in kidneys that subsequently developed death-censored graft failure. Thus, further understanding the role of these metabolites may inform the HMP process and help improve the objective evaluation of allograft offers, thereby reducing the discard of potentially viable organs. Hypothermic machine perfusion (HMP) is commonly used in the process of deceased donor kidney transplantation to preserve the organ during transport and help minimize cold ischemic injury to the allograft. HMP circulates cold perfusate solution through the graft kidney for several hours after removal from the donor and before transplantation into the recipient, and there is evidence that HMP reduces delayed graft function (DGF) and improves 3-year graft survival compared with static cold storage.1Peng P. Ding Z. He Y. et al.Hypothermic machine perfusion versus static cold storage in deceased donor kidney transplantation: a systematic review and meta-analysis of randomized controlled trials.Artif Organs. 2019; 43: 478-489Crossref PubMed Scopus (38) Google Scholar, 2Ravaioli M. De Pace V. Angeletti A. et al.Hypothermic oxygenated new machine perfusion system in liver and kidney transplantation of extended criteria donors: first Italian clinical trial.Sci Rep. 2020; 10: 6063Crossref PubMed Scopus (54) Google Scholar, 3Tingle S.J. Figueiredo R.S. Moir J.A. et al.Hypothermic machine perfusion is superior to static cold storage in deceased donor kidney transplantation: a meta-analysis.Clin Transplant. 2020; 34e13814Crossref PubMed Scopus (28) Google Scholar However, the biological mechanisms that underlie how HMP contributes to improved kidney graft health are unclear, with some studies suggesting possible roles for downregulation of inflammatory pathways and upregulation of cell survival pathways.4Fu Z. Ye Q. Zhang Y. et al.Hypothermic machine perfusion reduced inflammatory reaction by downregulating the expression of matrix metalloproteinase 9 in a reperfusion model of donation after cardiac death.Artif Organs. 2016; 40: E102-E111Crossref PubMed Scopus (17) Google Scholar,5Zhang Y. Fu Z. Zhong Z. et al.Hypothermic machine perfusion decreases renal cell apoptosis during ischemia/reperfusion injury via the Ezrin/AKT pathway.Artif Organs. 2016; 40: 129-135Crossref PubMed Scopus (23) Google Scholar Furthermore, although some perfusate proteins have demonstrated moderate predictive ability for DGF, no biomarkers have been associated with graft survival yet.6Guzzi F. Knight S.R. Ploeg R.J. Hunter J.P. A systematic review to identify whether perfusate biomarkers produced during hypothermic machine perfusion can predict graft outcomes in kidney transplantation.Transplant Int. 2020; 33: 590-602Crossref PubMed Scopus (25) Google Scholar The need for noninvasive biomarkers to assess allograft viability is greater than ever, as both the fraction of kidneys pumped and discarded increases.7Cooper M. Formica R. Friedewald J. et al.Report of National Kidney Foundation consensus conference to decrease kidney discards.Clin Transplant. 2019; 33e13419Crossref Scopus (57) Google Scholar,8Chang A. Schaubel D.E. Chen M. et al.Trends and outcomes of hypothermic machine perfusion preservation of kidney allografts in simultaneous liver and kidney transplantation in the United States.Transpl Int. 2022; 3510345Crossref Scopus (2) Google Scholar Even more recently, the change in the kidney allocation system to prioritize candidates within a distance of 250 nautical miles of the donor hospital has led to longer cold ischemia time and increased donor Kidney Donor Profile Indexes (KDPIs).9Adler J.T. Husain S.A. King K.L. Mohan S. Greater complexity and monitoring of the new Kidney Allocation System: implications and unintended consequences of concentric circle kidney allocation on network complexity.Am J Transplant. 2021; 21: 2007-2013Abstract Full Text Full Text PDF PubMed Scopus (34) Google Scholar,10Rohan V.S. Pilch N. McGillicuddy J. et al.Early assessment of national kidney allocation policy change.J Am Coll Surg. 2022; 234: 565-570Crossref PubMed Scopus (6) Google Scholar Given that marginal kidneys are more likely to be pumped, HMP use for organ preservation will likely further increase.11Sharma N. Mahajan A. Qazi Y.A. Marginal kidney transplantation: the road less traveled.Curr Opin Organ Transplant. 2019; 24: 92-96Crossref PubMed Scopus (9) Google Scholar However, the lack of standardized guidelines for HMP use has led to significant variability among organ procurement organizations (OPOs) in the selection of organs for HMP and the use of biomarkers and other parameters to monitor perfusion. Following the logistical challenges associated with its implementation, ≈30% of perfused kidneys nationally are later discarded because of concerns about organ quality and viability.12Woodside K.J. Merion R.M. Leichtman A.B. et al.Utilization of kidneys with similar kidney donor risk index values from standard versus expanded criteria donors.Am J Transplant. 2012; 12: 2106-2114Abstract Full Text Full Text PDF PubMed Scopus (39) Google Scholar Physical parameters, such as changes in kidney resistance and perfusate flow rates, are used to monitor the HMP process and infer the viability of kidneys on HMP.13Jochmans I. Moers C. Smits J.M. et al.The prognostic value of renal resistance during hypothermic machine perfusion of deceased donor kidneys.Am J Transplant. 2011; 11: 2214-2220Abstract Full Text Full Text PDF PubMed Scopus (131) Google Scholar However, flow and resistance parameters are imperfect, nonstandardized measures that vary over time and do not appear to be linked to the biological health of the graft.14Parikh C.R. Hall I.E. Bhangoo R.S. et al.Associations of perfusate biomarkers and pump parameters with delayed graft function and deceased donor kidney allograft function.Am J Transplant. 2016; 16: 1526-1539Abstract Full Text Full Text PDF PubMed Scopus (67) Google Scholar Developing our understanding of the active biological processes that occur during HMP and identifying key biomarkers directly linked to graft metabolism could improve the ability of transplant physicians to prognosticate allograft outcomes, design better perfusion solutions and strategies, and improve decision making around the use of HMP kidneys.15Arykbaeva A.S. de Vries D.K. Doppenberg J.B. et al.Metabolic needs of the kidney graft undergoing normothermic machine perfusion.Kidney Int. 2021; 100: 301-310Abstract Full Text Full Text PDF PubMed Scopus (13) Google Scholar,16De Deken J. Kocabayoglu P. Moers C. Hypothermic machine perfusion in kidney transplantation.Curr Opin Organ Transplant. 2016; 21: 294-300Crossref PubMed Scopus (50) Google Scholar As such, there is growing interest regarding how metabolite levels in the perfusate may reflect ongoing graft metabolism and function, and prior exploratory studies have demonstrated the metabolic activity of the kidney during HMP.15Arykbaeva A.S. de Vries D.K. Doppenberg J.B. et al.Metabolic needs of the kidney graft undergoing normothermic machine perfusion.Kidney Int. 2021; 100: 301-310Abstract Full Text Full Text PDF PubMed Scopus (13) Google Scholar,17Guy A.J. Nath J. Cobbold M. et al.Metabolomic analysis of perfusate during hypothermic machine perfusion of human cadaveric kidneys.Transplantation. 2015; 99: 754-759Crossref PubMed Scopus (43) Google Scholar Untargeted metabolomic profiling of biochemicals, which are 50 to 1000 Da in size, offers an efficient approach to exploring the active metabolic processes of kidney graft injury and repair in HMP. HMP perfusate offers an opportunity to follow the progressive changes of the allograft biological milieu as it responds to ischemic stress while remaining metabolically active. During HMP, there are changes in the composition of perfusate solution, which include uptake by the kidney of nutrients, such as amino acids and glucose, to supply ongoing cell processes, release of products of metabolism and degradation, or release of cellular contents and membranes in ischemic necrosis. Interrogation of perfusate fluid allows allograft-specific biological insight beyond pump parameters and clinical characteristics, such as those included in the Kidney Donor Risk Index (KDRI). Furthermore, the allograft's response to ischemic stress during HMP may provide insight to its subsequent response to additional stressors within the recipient after transplantation. Metabolic profiling would thus provide insights into the trajectory of graft function and outcomes. Using metabolomic profiling of kidney perfusate samples collected at the beginning and end of perfusion from 2 OPOs, we aimed to characterize metabolites that may reflect pathophysiological changes in the kidney during the duration of HMP. First, we explored the variability in the relatively novel matrix of kidney perfusate solution in both duplicate samples and paired left/right kidney samples. Second, we evaluated the association between changes in metabolites during HMP and death-censored graft failure (dcGF) and characterized differentially active metabolic processes in those kidneys that subsequently failed. Third, we performed secondary analyses on metabolites associated with dcGF for interaction with donation after cardiac death (DCD) status, association with DGF, and kidney discard. This is an ancillary study to the Deceased Donor Study, an ongoing multicenter, observational, cohort study of deceased donors and their kidney recipients enrolled between 2010 and 2013. The Deceased Donor Study population and methods have been described in detail elsewhere.14Parikh C.R. Hall I.E. Bhangoo R.S. et al.Associations of perfusate biomarkers and pump parameters with delayed graft function and deceased donor kidney allograft function.Am J Transplant. 2016; 16: 1526-1539Abstract Full Text Full Text PDF PubMed Scopus (67) Google Scholar,18Hall I.E. Akalin E. Bromberg J.S. et al.Deceased-donor acute kidney injury is not associated with kidney allograft failure.Kidney Int. 2019; 95: 199-209Abstract Full Text Full Text PDF PubMed Scopus (57) Google Scholar, 19Hall I.E. Schröppel B. Doshi M.D. et al.Associations of deceased donor kidney injury with kidney discard and function after transplantation.Am J Transplant. 2015; 15: 1623-1631Abstract Full Text Full Text PDF PubMed Scopus (101) Google Scholar, 20Reese P.P. Hall I.E. Weng F.L. et al.Associations between deceased-donor urine injury biomarkers and kidney transplant outcomes.J Am Soc Nephrol. 2016; 27: 1534-1543Crossref PubMed Scopus (74) Google Scholar The investigator team worked closely with 2 OPOs (New York Organ Donor Network [OPO-1], New York, NY; and Gift of Life Michigan [OPO-2], Ann Arbor, MI) that collected perfusate samples. The cohort was composed of transplanted kidneys that underwent HMP, from donors at least 16 years of age whose surrogates provided consent for research. Deceased donors were included if at least 1 kidney underwent HMP. Kidneys were excluded if no perfusate samples were obtained. OPO personnel followed institutional protocols for managing donors. The study was approved by OPO scientific review committees and institutional review boards for the investigators. All kidneys were individually pumped using the LifePort Kidney Transporter (Organ Recovery Systems). OPO personnel managed the perfusion machines according to the OPO's protocol. The Supplementary Methods include additional details about perfusion methods. Perfusate samples were collected from the perfusion machine at 2 time points: 1 sample within 10 minutes of starting perfusion, referred to as the baseline sample; and a second sample just before the OPO transferred management of the kidney to the recipient center, referred to as the post-HMP sample. The timing of sample collections was recorded by OPO personnel. Each sample was transported on ice and stored at –80 °C at the OPO until monthly batch shipments to the coordinating center. Samples were subsequently processed at the coordinating center following a single controlled thaw, separated into bar-coded aliquots, and stored at −80 °C without the addition of protease inhibitors until metabolite measurement. All samples were measured by Metabolon Inc using high-performance liquid chromatography/tandem accurate mass spectrometry methods, as described previously.21Evans A.M. DeHaven C.D. Barrett T. et al.Integrated, nontargeted ultrahigh performance liquid chromatography/electrospray ionization tandem mass spectrometry platform for the identification and relative quantification of the small-molecule complement of biological systems.Anal Chem. 2009; 81: 6656-6667Crossref PubMed Scopus (998) Google Scholar All analysis and preprocessing of spectral peaks to quantify biochemicals in samples were performed by Metabolon Inc. Four samples of perfusate solution, which we refer to as stock samples, were measured alongside the kidney perfusate samples collected during HMP. We performed sample quality control based on the consistency of baseline and post-HMP perfusate time labels and excluded samples for which the labeled collection time of baseline perfusate was missing or invalid. In addition, we also performed measurements of 42 pairs of blind duplicate samples to quantify % coefficients of variation for each metabolite. Additional measurement details are provided in the Supplementary Methods. To ascertain donor and recipient characteristics and outcomes, we linked study databases to the United Network for Organ Sharing database. The United Network for Organ Sharing is contracted to supply data to the Organ Procurement and Transplantation Network. The Organ Procurement and Transplantation Network data system includes data on all donors, wait-listed candidates, and transplant recipients in the United States, submitted by the members of the Organ Procurement and Transplantation Network, and has been described elsewhere. The Health Resources and Services Administration, US Department of Health and Human Services, provides oversight to the activities of the Organ Procurement and Transplantation Network contractor. Through review of OPO charts, we augmented donor data with additional elements, including admission serum creatinine level and perfusion time. We performed medical record review on recipients at participant transplant centers in our study network who received kidneys from enrolled donors. Trained site coordinators reviewed medical records and recorded detailed recipient characteristics and outcomes. Our primary outcome was dcGF, defined as return to dialysis after transplantation during the study follow-up. The loss of the kidney graft due to recipient death was censored in this analysis. In a secondary analysis, we also used the outcome of DGF, defined by the need for >1 dialysis session in the first week or a serum creatinine reduction ratio <25% within the first 48 hours after transplantation.22Hall I.E. Reese P.P. Doshi M.D. et al.Delayed graft function phenotypes and 12-month kidney transplant outcomes.Transplantation. 2017; 101: 1913-1923Crossref PubMed Scopus (34) Google Scholar Descriptive statistics were reported as mean (SD) or median (interquartile range [IQR]) for continuous variables and as frequency (percentage) for categorical variables. We calculated the KDRI based on the following donor characteristics: age, sex, race, height, weight, history of hypertension, history of diabetes, hepatitis C serostatus, stroke as the cause of death, donation after cardiac determination of death status, and terminal serum creatinine level.23OPTN. A guide to calculating and interpreting the Kidney Donor Profile Index (KDPI). Accessed July 1, 2021. https://optn.transplant.hrsa.gov/media/1512/guide_to_calculating_interpreting_kdpi.pdfGoogle Scholar We calculated the 2010 KDPI from the KDRI, as per convention.23OPTN. A guide to calculating and interpreting the Kidney Donor Profile Index (KDPI). Accessed July 1, 2021. https://optn.transplant.hrsa.gov/media/1512/guide_to_calculating_interpreting_kdpi.pdfGoogle Scholar For metabolites with >50% of samples above the minimum detected level in post-HMP perfusate measurements, we scaled biochemical measurements such that 1 unit equals 1 median absolute deviation. In evaluating outcome associations, metabolites with ≤50% samples above the minimum detected level (105 of 388 metabolites) were dichotomized. Nondichotomized metabolites are referred to as continuous metabolites. To estimate the dcGF hazard ratio (HR) per median absolute deviation, we fit Cox proportional hazard models adjusted for perfusion time, and then additionally for KDPI. We accounted for the cluster effect of paired kidneys from the same donor using robust sandwich estimates by donor. De novo metabolites were considered significantly associated with dcGF after false-discovery correction. Additional details on analyses can be found in the Supplementary Methods. The study was approved by the institutional review boards of all participating institutions, and written informed consent was obtained from all participants or their surrogates. Perfusate samples from all 197 kidneys that underwent HMP and were transplanted were selected for untargeted metabolomic analysis (Figure 1a ). Perfusate samples from 35 discarded kidneys, matched by OPO and KDRI, were also analyzed to compare discarded versus dcGF and non-dcGF kidneys. After sample quality control, 7 samples from transplanted kidneys were excluded, and perfusate samples from 190 individually transplanted kidneys and 35 discarded kidneys that underwent HMP were included. The transplanted kidneys in the study were contributed by 147 deceased donors, whose characteristics are provided in Table 1. Mean KDRI was 1.42 ± 0.43, and mean admission-to-procurement time was 6.6 ± 5 days. Individual kidneys underwent 9.9 ± 5.7 hours of HMP before transplantation. The recipient cohort for the 190 HMP kidneys were a mean age of 57 ± 14 years, 65% were men, 45% were Black race, and mean duration on dialysis was 58 ± 37 months (Table 1). The donor characteristics of 35 discarded kidneys are presented in Supplementary Table S1.Table 1Donor and recipient characteristicsDeceased donor characteristicsValue (N = 147)Recipient and transplant characteristicsValue (N = 190)Age, yr47 ± 14Age, yr57 ± 14Male sex93 (63)Male sex124 (65)Black race26 (18)Black race86 (45)Hispanic ethnicity28 (19)Hispanic ethnicity21 (11)BMI, kg/m229 ± 8BMI, kg/m229 ± 6Hypertension64 (44)Cause of kidney failureDiabetes16 (11) Diabetes23 (12)Cause of death Hypertension70 (37) Head trauma28 (19.0) Glomerulonephritis52 (27) Anoxia55 (37.4) Graft failure26 (14) Stroke60 (40.8) Other or unknown19 (10) Other4 (2.7)Preemptive transplant0 (0)Hepatitis C–seropositive0 (0)Dialysis duration, mo58 ± 37ECD42 (29)Pretransplantation blood transfusion46 (24)DCD27 (18)PRA, %KDRI1.42 ± 0.43 0136 (71.6)KDPI, %58 ± 26 1–2012 (6.3)KDPI ≥8036 (24) 21–8018 (9.5) >8024 (12.6)Admission to procurement, d7 ± 5Cold ischemia time, h17.9 ± 7.3Admission SCr, mg/dl1.11 ± 0.41Pump duration, h9.9 ± 5.7Terminal SCr, mg/dl1.44 ± 1.22HLA mismatchUrine creatinine, mg/dl59.26 ± 55.70 02 (1)No. of kidneys discarded 12 (1) 0130 (88) 26 (3) 117 (12) 319 (10)Donor AKI stage 450 (26) No AKI106 (72) 577 (41) Stage 123 (16) 634 (18) Stage 27 (5) Stage 311 (7)AKI, acute kidney injury; BMI, body mass index; DCD, donation after cardiovascular determination of death; ECD, expanded-criteria donor; HLA, human leukocyte antigen; KDPI, Kidney Donor Profile Index; KDRI, Kidney Donor Risk Index; PRA, panel-reactive antibody; SCr, serum creatinine.Values are mean ± SD or n (%). Open table in a new tab AKI, acute kidney injury; BMI, body mass index; DCD, donation after cardiovascular determination of death; ECD, expanded-criteria donor; HLA, human leukocyte antigen; KDPI, Kidney Donor Profile Index; KDRI, Kidney Donor Risk Index; PRA, panel-reactive antibody; SCr, serum creatinine. Values are mean ± SD or n (%). Metabolomics analysis quantified 629 unique metabolites from perfusate samples, 76 of which were unknown chemicals and were excluded from the analysis (Figure 1b). Of the 553 known metabolites, 165 were identified in the stock perfusate solution samples, and are referred to as "stock" metabolites. We refer to the other 388 metabolites as "de novo" metabolites, which appeared in perfusate while the graft was being perfused (Supplementary Appendix). The set of metabolites detected in samples from each of the 2 OPOs was consistent, with 547 (99%) metabolites detected among samples from OPO-1 and 537 (97%) metabolites detected among samples from OPO-2, demonstrating consistency in the biological processes across sites. The 553 known metabolites included 181 amino acids, 138 lipids, 114 xenobiotics, 30 carbohydrates, 27 cofactors/vitamins, 26 nucleotides, 20 peptides, 10 energy molecules, and 7 partially characterized molecules (Figure 2). Quantified molecules ranged from 74 to 835 Da in size. The median % coefficients of variation of continuous de novo and stock metabolites were 19.0 (IQR, 15.4–23.4) and 16.5 (IQR, 11.6–22.6), respectively (Supplementary Appendix). Of known metabolites identified in perfusate, 13% (74) demonstrated significant associations between concentration changes during perfusion and perfusion time (P < 0.05). Of these changes, 90% (67) were increases in concentration over time with continued perfusion (Supplementary Appendix). We compared the similarity of paired donor kidneys versus nonpaired kidneys to explore the consistency of the perfusate metabolome through correlation and clustering. Among the 112 kidney pairs from 56 donors, the post-HMP perfusate metabolites were strongly correlated, with overall median rs = 0.88 (IQR, 0.82–0.91), whereas nonpaired kidneys were weakly correlated, with median rs = 0.13 (IQR, –0.01 to 0.26). After adjustment for paired perfusion time differences, the metabolites remained strongly correlated, with median R2 = 0.78 (IQR, 0.59–0.89). Among the metabolites, the median Pearson correlation between paired left and right kidneys was 0.84 (IQR, 0.73–0.92), altogether suggesting a biological consistency of the perfusate metabolome between left and right kidneys (Supplementary Appendix). We performed unsupervised agglomerative clustering to explore patterns and structures among postperfusate metabolomic profiles. Overall, 50 of 56 kidney pairs (87%) formed unsupervised pairs (Supplementary Figure S1). The post-HMP perfusate metabolome of paired kidneys was not only strongly correlated, but also uniquely distinguishable from other donor kidneys, suggesting that perfusate metabolomic profiles were specific to donor biology. Of the 190 recipients, 33 (17%) experienced dcGF over a median follow-up of 5.0 (IQR, 3.0–6.1) years. The event rate for dcGF was 38.1 (95% confidence interval, 26.3–56.6) per 1000 person-years. After adjusting for duration of HMP, we observed significant associations with dcGF in post-HMP perfusate metabolites for 6 metabolites with false discovery rate–corrected P < 0.05 (q < 0.05) (Table 2) and for 40 metabolites with P < 0.05 (Figure 3 and Supplementary Appendix). These metabolites include α-ketoglutarate (AKG), 3-carboxy-4-methyl-5-propyl-2-furanpropanoate (CMPF), 1-carboxyethylphenylalanine, 1-palmitoyl-2-docosahexaenoyl-glycerophosphocholine (GPC) (16:0/22:6), 1-palmitoyl-2-dihomo-linolenoyl-GPC (16:0/20:3n3 or 6), and 1-stearoyl-2-docosahexaenoyl-GPC (18:0/22:6). No significant interactions with OPO (P < 0.1) were found for these metabolites. The strongest association was seen with AKG, which had a 45% increased risk for dcGF per each median absolute deviation increase. Although no metabolites were associated with reduced risk of dcGF after false discovery correction, 26 (65%) of the 40 metabolites with dcGF association P < 0.05 were associated with reduced risk (Supplementary Appendix).Table 2Post-HMP perfusate metabolite association with dcGFMetaboliteDetected in samples, %Mean fold elevation in dcGFSubpathwaydcGF HR per MAD (95% CI)+
In the United States, universal screening for human T-lymphotropic virus (HTLV) in deceased organ donors was discontinued in 2009. Since then, the transplant guideline suggests considering targeted screening. However, the outcomes of this change in HTLV screening have not been evaluated.
